Multitask Instruction-based Prompting for Fallacy Recognition
This work addresses the problem of fallacy recognition for computational models, which is incremental as it adapts existing prompting methods to a multitask setup for a specific domain.
The paper tackled the challenge of recognizing fallacies across diverse datasets with varying formats, genres, and fallacy types by using a multitask instruction-based prompting approach based on the T5 model, which improved results against dataset-specific models like T5, BERT, or GPT-3 and recognized 28 unique fallacies.
Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. Recognizing fallacies is an intrinsically difficult task both for humans and machines. Moreover, a big challenge for computational models lies in the fact that fallacies are formulated differently across the datasets with differences in the input format (e.g., question-answer pair, sentence with fallacy fragment), genre (e.g., social media, dialogue, news), as well as types and number of fallacies (from 5 to 18 types per dataset). To move towards solving the fallacy recognition task, we approach these differences across datasets as multiple tasks and show how instruction-based prompting in a multitask setup based on the T5 model improves the results against approaches built for a specific dataset such as T5, BERT or GPT-3. We show the ability of this multitask prompting approach to recognize 28 unique fallacies across domains and genres and study the effect of model size and prompt choice by analyzing the per-class (i.e., fallacy type) results. Finally, we analyze the effect of annotation quality on model performance, and the feasibility of complementing this approach with external knowledge.